Remote sensing image registration based on dual-channel neural network and robust point set registration algorithm

被引：3

作者：

Wang Dongzhen ^{[1
]}

Chen Ying ^{[1
]}

Li Jipeng ^{[1
]}

机构：

[1] Shanghai Inst Technol, Dept Comp Sci & Informat Engn, Shanghai, Peoples R China

来源：

2020 5TH INTERNATIONAL CONFERENCE ON INTELLIGENT INFORMATICS AND BIOMEDICAL SCIENCES (ICIIBMS 2020) | 2020年

关键词：

Remote sensing image; Image registration; Dense structure; Dual-channel network; Thin plate spline; Robust point set registration;

D O I：

10.1109/ICIIBMS50712.2020.9336411

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Remote sensing image registration technology has important applications in military and civilian fields such as ground target recognition, urban development evaluation, and geographic change evaluation. In this paper, a remote sensing image registration method based on dual-channel convolutional neural network (DCCNN) is proposed. Firstly, the dual-channel neural network model with improved dense structure is used to extract the features of the input image pair and generate the corresponding feature points. Then the affine transformation coefficient is obtained by feature matching using the robust point set registration algorithm (TPS-RPM) based on thin-plate spline. Finally, the image to be registered can be transformed according to the coefficient to achieve the purpose of registration.The experimental results show that the registration accuracy of this method is higher than that of the comparison method.

引用

页码：208 / 215

页数：8

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